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Containerized AI and Machine Learning

  1. 0

    Containerized AI and Machine Learning: Overview

    In this article – the first one of the series – we’ll go over some Docker basics as they apply to ML applications.
    Added 26 Apr 2021
  2. 1

    Creating Docker Containers for AI and Machine Learning

    In this article, we’ll start applying our basic Docker knowledge while creating and running containers in the various MLng scenarios.
    Added 26 Apr 2021
  3. 2

    Running AI Models in Docker Containers

    In this article, we’ll create a container to run a CPU inference on the trained model.
    Added 27 Apr 2021
  4. 3

    Running AI Models in Docker Containers on ARM Devices

    In this article, we’ll adapt our image for Raspberry Pi with an ARM processor.
    Added 28 Apr 2021
  5. 4

    Running AI Models in GPU-Enabled Docker Containers

    In this article we go back to the Intel/AMD CPUs. This time, we will speed up our calculations using a GPU.
    Added 29 Apr 2021
  6. 5

    Multi-Stage Docker Builds for AI Object Detection

    In this article we run inference on sample images with TensorFlow using a containerized Object Detection API environment.
    Added 18 May 2021
  7. 6

    Dockerized AI on Large Models With NLP and Transformers

    In this article we run an inference model for NLP using models persisted on a Docker volume.
    Added 19 May 2021
  8. 7

    Exposing Dockerized AI Models via RESTful API

    In this article, we’ll modify our code to expose the same logic via a Rest API service.
    Added 20 May 2021
  9. 8

    Debugging AI Code Running in a Docker Container

    In this article we use Visual Studio Code to edit and debug our increasingly complex code running inside a Docker container.
    Added 21 May 2021
  10. 9

    Deploying AI Docker Containers to the Cloud

    In this article, we publish our NLP API service to Azure using Azure Container Instances.
    Added 25 May 2021